检索结果-内蒙古大学图书馆

Fast Learning Network Algorithm for Voice Pathology Detection and Classification

Multimedia Tools and Applications 2024年 1-32页

作者： Albadr, Musatafa Abbas Abbood Ayob, Masri Tiun, Sabrina AL-Dhief, Fahad Taha Al-Daweri, Muataz Salam Homod, Raad Z. Abbas, Ali Hashim Department of Petroleum Project Management College of Industrial Management of Oil and Gas Basrah University for Oil and Gas Al-Basrah Iraq Faculty of Information Science and Technology CAIT Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Department of Communication Engineering School of Electrical Engineering Universiti Teknologi Malaysia UTM Johor Bahru Johor Malaysia Department of Information Systems College of Economics Management & ampInformation Systems University of Nizwa Nizwa Oman Department of Oil and Gas Engineering Basrah University for Oil and Gas Al-Basrah Iraq Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna Iraq

The utilisation of ML (Machine Learning) techniques in the detection of the VP (Voice Pathology) has recently gained a lot of consideration. However, these efforts still have several drawbacks such as: I) the accuracy rates of most previous works are still not promising and need more improvement. II) The majority of the previous works have concentrated on the task of VP detection only and disregarded the task of VP classification. III) Most of the previous works have been assessed based on a single voice data only like the vowel /a/, and the other vowels (/i/, and /u/) and sentences were disregarded. IV) The majority of the previous works performance were assessed utilising a limited evaluation metrics. Recently, one of the utmost effective ML techniques is FLN (Fast Learning Network), it is an efficient technique for data classification. However, the FLN classifier has not been implemented to the problem of VP detection and classification. Therefore, this research proposes the FLN classifier with MFCC (Mel-Frequency Cepstral Coefficient) features in order to enhance the accuracy of the VP detection and classification. The FLN classifier has the ability to a) solve both binary and multiclass classification issues, b) eliminate overfitting, as well as c) operate akin to a NN (Neural Network) structure while employing the principles of a kernel-based SVM (Support Vector Machine). In this research, the SVD (Saarbrucken Voice Database) dataset was utilised to assess the FLN classifier performance in the VP detection and classification. The assessment of the proposed FLN classifier was conducted based on two phases: the first phase includes all the voice samples of the SVD dataset with the sentences and vowels (i.e., /a/, /i/, and /u/) which are pronounced in neutral, high, and low pitches. While the second phase uses the voice samples of the utmost common 3 pathology types (i.e., paralysis, polyp, and cyst) based on the vowel /a/ that is pronounced in neutral pitch. The

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

A Review of the Multiple Criteria for the Design of Resilient Buildings

A Review of the Multiple Criteria for the Design of Resilien...

引用

2024 IEEE International Conference on Technology management, Operations and Decisions, ICTMOD 2024

作者： Abdulmaksoud, Sara Akram, Fayqa Ahmed, Vian Engineering Systems Management American University of Sharjah Sharjah United Arab Emirates American University of Sharjah Department of Industrial Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350367355

Climate change and its repercussions, namely meteorological hazards such as floods, are occurring more severely and frequently compared to any point in history, due to anthropogenic activities such as the increase in urbanization rates. These floods inevitably have adverse impacts on the built environment and have resulted in immense financial damage and a sizable number of fatalities globally. Henceforth, resilience against floods is of paramount significance in the built environment. Specifically, 'resilience' in the design of buildings has become a growing theme and is deemed a powerful tool for survivability against the current climate crisis. Similarly, the United Arab Emirates (UAE) is also becoming vulnerable to meteorological disasters such as floods, which are aggravated by the substantial urbanization occurring. Therefore, the adoption of resilient design strategies in building design is a mandatory undertaking in the UAE. For the successful implementation of resilience in buildings, a thorough understanding of what resilience is in a building design context is vital. However, in the UAE, there is still no clear understanding of what resilience is in the construction industry or what criteria are essential for designing resilient buildings, and this is attributed to the lack of tools that could assist in the implementation of resilient building design strategies. Designing for resilience is a significant challenge and therefore, scholars have emphasized the importance of implementing suitable decision support tools to guide the stakeholders in the construction industry to successfully achieve favorable resilient design decisions. Accordingly, upon reviewing the previously conducted research and frameworks that discuss the various applications of design strategies for resilient buildings to counteract the risk of catastrophes such as floods, this paper aims to underpin the criteria that define the application of resilience in the different building componen

关键词： Building components

来源：评论

学校读者我要写书评

暂无评论

Adoption of cognitive computing decision support system in the assessment of health-care policymaking

引用

Journal of systems and Information Technology 2023年第4期25卷 395-439页

作者： Behera, Rajat Kumar Bala, Pradip Kumar Panigrahi, Prabin Kumar Dasgupta, Shilpee A. School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Department of Information Systems and Business Analytics Indian Institute of Management Ranchi Ranchi India Department of Information Systems Indian Institute of Management Indore Indore India Department of General Management Indian Institute of Management Ranchi Ranchi India

Purpose: Despite technological advancements to enhance patient health, the risks of not discovering the correct interactions and trends in digital health are high. Hence, a careful policy is required for health coverage tailored to needs and capacity. Therefore, this study aims to explore the adoption of a cognitive computing decision support system (CCDSS) in the assessment of health-care policymaking and validates it by extending the unified theory of acceptance and use of technology model. Design/methodology/approach: A survey was conducted to collect data from different stakeholders, referred to as the 4Ps, namely, patients, providers, payors and policymakers. Structural equation modelling and one-way ANOVA were used to analyse the data. Findings: The result reveals that the behavioural insight of policymakers towards the assessment of health-care policymaking is based on automatic and reflective systems. Investments in CCDSS for policymaking assessment have the potential to produce rational outcomes. CCDSS, built with quality procedures, can validate whether breastfeeding-supporting policies are mother-friendly. Research limitations/implications: Health-care policies are used by lawmakers to safeguard and improve public health, but it has always been a challenge. With the adoption of CCDSS, the overall goal of health-care policymaking can achieve better quality standards and improve the design of policymaking. Originality/value: This study drew attention to how CCDSS as a technology enabler can drive health-care policymaking assessment for each stage and how the technology enabler can help the 4Ps of health-care gain insight into the benefits and potential value of CCDSS by demonstrating the breastfeeding supporting policy. © 2023, Emerald Publishing Limited.

关键词： Decision support systems

来源：评论

学校读者我要写书评

暂无评论

ADVANCING AUTONOMOUS ROBOTICS: INTEGRATING MECANUM WHEELS, LIDAR, AND SLAM FOR ENHANCED MOBILITY AND NAVIGATION THROUGH STUDENT INVOLVEMENT

ADVANCING AUTONOMOUS ROBOTICS: INTEGRATING MECANUM WHEELS, L...

引用

ASME 2024 International Mechanical engineering Congress and Exposition, IMECE 2024

作者： Chiou, Richard Y. Vasudevan, Nijanthan Kavitha, Arjuna Karthikeyan Senthilvel Tseng, Tzu-Liang Department of Engineering Leadership and Society Engineering Technology Program Drexel University PhiladelphiaPA United States Department of Industrial Manufacturing and Systems Engineering University of Texas at El Paso El PasoTX United States

ISBN: (纸本)9780791888650

This study unveils an advanced autonomous mobile robot (AMR) designed as a practical educational module for students, featuring Mecanum wheels, LiDAR, and integrated through a comprehensive SLAM algorithm, all within the framework of the Robot Operating System (ROS). The AMR's use of Mecanum wheels affords it unparalleled agility and the capability to maneuver omnidirectionally in intricate settings. LiDAR technology enhances the robot's ability to generate detailed spatial maps, crucial for real-time navigation and obstacle avoidance. Through experimental validation in a controlled laboratory environment, the AMR demonstrated superior navigational accuracy and adaptability to evolving conditions. This hands-on educational approach not only underlines the robot's effectiveness in navigating real-world challenges but also positions it as an adaptable solution for future automation tasks, emphasizing the potential for its application across a broad spectrum of industrial and commercial fields. Copyright © 2024 by ASME.

关键词： SLAM robotics

来源：评论

学校读者我要写书评

暂无评论

Nonparametric Probabilistic Prediction of Regional PV Outputs Based on Granule-based Clustering and Direct Optimization programming

引用

Journal of Modern Power systems and Clean Energy 2023年第5期11卷 1450-1461页

作者： Yonghui Sun Yan Zhou Sen Wang Rabea Jamil Mahfoud Hassan Haes Alhelou George Sideratos Nikos Hatziargyriou Pierluigi Siano College of Energy and Electrical Engineering Hohai UniversityNanjing 210098China College of Water Conservancy and Hydropower Engineering Hohai UniversityNanjing 210098China IEEE Department of Electrical Engineering Tishreen UniversityLatakia 2230Syria National Technical University of Athens Athens 15773Greece Department of Management&Innovation Systems University of SalernoSalerno 84084Italy

Regional photovoltaic(PV) power prediction plays an important role in power system planning and operation. To effectively improve the performance of prediction intervals(PIs) for very short-term regional PV outputs, an efficient nonparametric probabilistic prediction method based on granulebased clustering(GC) and direct optimization programming(DOP) is proposed. First, GC is proposed to formulate and cluster the sample granules consisting of numerical weather prediction(NWP) and historical regional output data, for the enhanced hierarchical clustering performance. Then, to improve the accuracy of samples' utilization, an unbalanced extension is used to reconstruct the training samples consisting of power time series. After that, DOP is applied to quantify the output weights based on the optimal overall performance. Meanwhile, a balance coefficient is studied for the enhanced reliability of PIs. Finally, the proposed method is validated through multistep PIs based on the numerical comparison of real PV generation data.

关键词： Regional photovoltaic output prediction intervals granule-based clustering direct optimization programming nonparametric probabilistic prediction

来源：评论

学校读者我要写书评

暂无评论

Intelligent traffic prediction system using hybrid convolutional neural networks for smart cities

引用

Multimedia Tools and Applications 2024年 1-19页

作者： J, Jeba Sonia G, Arun Kumar kumar, ERajesh Raju, K. Narasimha Sudha, V. Kshirsagar, Pravin R. Tirth, Vineet Rajaram, A. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chennai603203 India Department of Electronics and Communication Engineering JSS Academy of Technical Education Uttar Pradesh Noida201301 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Andhra Pradesh Visakhapatnam530048 India Department of Electronics and Communication Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Electronics & amp Telecommunication Engineering J D College of Engineering & ampManagement Maharashtra Nagpur India Mechanical Engineering Department Centre for Engineering and Technology Innovations College of Engineering King Khalid University Asir Abha61421 Saudi Arabia Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India

Intelligent Traffic management systems (ITMS) play a crucial role in advancing smart city applications by addressing the rapid increase in road traffic due to the growing number of vehicles. Effective real-time traffic prediction is essential to avoid congestion, yet existing systems often struggle with high delays and insufficient accuracy. This paper introduces an innovative ITMS based on a Hybrid Convolutional Neural Network (HCNN) to overcome these challenges. The proposed system integrates numerous smart devices, including smart vehicles, with an edge computing framework where the HCNN model is deployed. The HCNN combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance prediction accuracy and reduce latency. Experimental results demonstrate that the HCNN model achieves up to 95% prediction accuracy while reducing delays by up to 50% compared to conventional methods. These improvements are significant for smart city applications, ensuring timely and precise traffic management. The study validates the effectiveness of the HCNN model in improving traffic prediction and proposes its potential for broader smart city implementation. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Importance measure-based maintenance strategy optimization:Fundamentals,applications and future directions in AI and IoT

引用

Frontiers of engineering management 2024年第3期11卷 542-567页

作者： Hongyan DUI Xinmin WU Shaomin WU Min XIE School of Management Zhengzhou UniversityZhengzhou 450001China Kent Business School University of KentCanterburyKent CT27FSUK Department of Systems Engineering and Engineering Management City University of Hong KongHong Kong 999077China

Numerous maintenance strategies have been proposed in the literature related to *** paper focuses on the utilization of reliability importance measures to optimize maintenance *** analyze maintenance strategies based on importance measures and identify areas lacking sufficient *** paper presents principles and formulas for advanced importance measures within the context of optimizing maintenance ***,it classifies methods of maintenance strategy optimization according to importance measures and outlines the roles of these measures in various maintenance ***,it discusses potential challenges that optimization of maintenance strategies based on importance measures may encounter with future technologies.

关键词： maintenance strategy importance measure reliability maintenance cost

来源：评论

学校读者我要写书评

暂无评论

A Bi-LSTM Autoencoder Framework for Anomaly Detection - A Case Study of a Wind Power Dataset 19

A Bi-LSTM Autoencoder Framework for Anomaly Detection - A Ca...

引用

19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Raihan, Ahmed Shoyeb Ahmed, Imtiaz West Virginia University The Department of Industrial & Management Systems Engineering WV United States

ISBN: (纸本)9798350320695

Anomalies refer to data points or events that deviate from normal and homogeneous events, which can include fraudulent activities, network infiltrations, equipment malfunctions, process changes, or other significant but infrequent events. Prompt detection of such events can prevent potential losses in terms of finances, information, and human resources. With the advancement of computational capabilities and the availability of large datasets, anomaly detection has become a major area of research. Among these, anomaly detection in time series has gained more attention recently due to the added complexity imposed by the time dimension. This study presents a novel framework for time series anomaly detection using a combination of Bidirectional Long Short Term Memory (Bi-LSTM) architecture and Autoencoder. The Bi-LSTM network, which comprises two unidirectional LSTM networks, can analyze the time series data from both directions and thus effectively discover the long-term dependencies hidden in the sequential data. Meanwhile, the Autoencoder mechanism helps to establish the optimal threshold beyond which an event can be classified as an anomaly. To demonstrate the effectiveness of the proposed framework, it is applied to a real-world multivariate time series dataset collected from a wind farm. The Bi-LSTM Autoencoder model achieved a classification accuracy of 96.79% and outperformed more commonly used LSTM Autoencoder models. © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Locating Emergency Response Facilities in the Metrorail System: A Decision Support Tool

Locating Emergency Response Facilities in the Metrorail Syst...

引用

2023 systems and Information engineering Design Symposium, SIEDS 2023

作者： Ottinger, Elizabeth R. Mora, Alejandro F. Medina Patel, Kaveena A. Van Dusen, Islay R. Gralla, Erica The George Washington University Department of Engineering Management and Systems Engineering United States George Washington University EMSE Department United States

ISBN: (纸本)9798350300642

Large public transportation systems, like that of the Washington Metropolitan Area Transit Authority (WMATA), must appropriately locate response personnel to respond quickly to emergencies throughout the Metrorail system. This is particularly challenging in sprawling and congested metropolitan areas like Washington, DC. The aim of this project is to support the WMATA Office of Emergency Preparedness (OEP) in determining appropriate geographic locations for response personnel with reduced response times to all areas of the Metrorail system. To that end, we developed a simulation model that evaluates response times to emergencies at WMATA Metrorail stations. The model relies on historical data of WMATA emergency incidents to generate probability distributions of incidents, and queries Google Maps application programming interface (API) using Python to provide responder travel times that account for the traffic at that time of day. The user inputs the proposed responder locations (one or several bases) and the tool outputs the response times to a set of emergencies. Resulting response times are then analyzed, visualized, and compared across scenarios, using response time distributions and geographic heat maps, to show response times for the system overall as well as specific stations or geographic areas. In collaboration with the WMATA OEP, we evaluate several scenarios involving moving their current OEP base to a more central location and/or allocating response personnel to different geographic areas. Based on these results, we recommend better locations for WMATA response personnel, which could improve response times by up to 27 minutes or 67% throughout the Metrorail system. While these results are specific to WMATA, the tool could be easily adapted to other public transit systems to support decisions on the location of emergency response personnel. © 2023 IEEE.

关键词： Location

来源：评论

学校读者我要写书评

暂无评论

The defender’s perspective on automatic speaker verification: An overview

The defender’s perspective on automatic speaker verificatio...

引用

2023 Workshop on Deepfake Audio Detection and Analysis, DADA 2023

作者： Wu, Haibin Kang, Jiawen Meng, Lingwei Meng, Helen Lee, Hung-Yi Graduate Institute of Communication Engineering National Taiwan University Taiwan Department of Systems Engineering & Engineering Management The Chinese University of Hong Kong Hong Kong

Automatic speaker verification (ASV) plays a critical role in security-sensitive environments. Regrettably, the reliability of ASV has been undermined by the emergence of spoofing attacks, such as replay and synthetic speech, as well as adversarial attacks and the relatively new partially fake speech. While there are several review papers that cover replay and synthetic speech, and adversarial attacks, there is a notable gap in a comprehensive review that addresses defense against adversarial attacks and the recently emerged partially fake speech. Thus, the aim of this paper is to provide a thorough and systematic overview of the defense methods used against these types of attacks. © 2023 CEUR-WS. All rights reserved.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：